{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,inspect\n",
    "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parentdir = os.path.dirname(currentdir)\n",
    "sys.path.insert(0,parentdir) \n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.autograd as autograd\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class CBOW(nn.Module):\n",
    "\n",
    "    def __init__(self, context_size=2, embedding_size=100, vocab_size=None):\n",
    "        super(CBOW, self).__init__()\n",
    "        self.embeddings = nn.Embedding(vocab_size, embedding_size)\n",
    "        self.linear1 = nn.Linear(embedding_size, vocab_size)\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        lookup_embeds = self.embeddings(inputs)\n",
    "        embeds = lookup_embeds.sum(dim=1)\n",
    "        out = self.linear1(embeds)\n",
    "        return out\n",
    "\n",
    "\n",
    "def make_context_vector(context, word_to_ix):\n",
    "    idxs = [word_to_ix[w] for w in context]\n",
    "    tensor = torch.LongTensor(idxs)\n",
    "    return autograd.Variable(tensor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(tokenized_words, epochs=100, context_size=2, embedding_size=10):\n",
    "    \"\"\"\n",
    "    context_size: x words to the left, 2 to the right\n",
    "    \"\"\"\n",
    "    vocab = set(tokenized_words)\n",
    "    vocab_size = len(vocab)\n",
    "\n",
    "    word_to_ix = {word: i for i, word in enumerate(vocab)}\n",
    "    data = []\n",
    "    for i in range(2, len(tokenized_words) - 2):\n",
    "        context = [tokenized_words[i - 2], tokenized_words[i - 1],\n",
    "                   tokenized_words[i + 1], tokenized_words[i + 2]]\n",
    "        target = tokenized_words[i]\n",
    "        data.append((context, target))\n",
    "\n",
    "    loss_func = nn.CrossEntropyLoss()\n",
    "    \n",
    "    net = CBOW(context_size, embedding_size=embedding_size, vocab_size=vocab_size)\n",
    "    optimizer = optim.SGD(net.parameters(), lr=0.01)\n",
    "    \n",
    "    print(\"Start training...\")\n",
    "    for epoch in range(epochs):\n",
    "        total_loss = 0\n",
    "        BSZ = 64\n",
    "        input_batch = []\n",
    "        target_batch = []\n",
    "        n_batches = 0\n",
    "        for context, target in data:\n",
    "            context_var = make_context_vector(context, word_to_ix)\n",
    "            target_ = torch.LongTensor([word_to_ix[target]])\n",
    "            # Build batches\n",
    "            input_batch.append(context_var)\n",
    "            target_batch.append(target_)\n",
    "            if len(input_batch) == BSZ:\n",
    "                # Run completed batch\n",
    "                input_batch = torch.stack(input_batch)\n",
    "                target_batch = torch.stack(target_batch).long().squeeze()\n",
    "\n",
    "                net.zero_grad()\n",
    "                log_probs = net(input_batch)\n",
    "                loss = loss_func(log_probs, target_batch)\n",
    "\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "                total_loss += loss.item()\n",
    "                \n",
    "                input_batch = []\n",
    "                target_batch = []\n",
    "                n_batches += 1\n",
    "        print(epoch, total_loss)\n",
    "    \n",
    "    return net, vocab, word_to_ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ptb import lang_util\n",
    "\n",
    "PTB_TRAIN_PATH = \"/Users/jgordon/nta/datasets/PTB/ptb.train.txt\"\n",
    "\n",
    "def tokenize(path):\n",
    "    \"\"\"Tokenizes a text file.\"\"\"\n",
    "    assert os.path.exists(path)\n",
    "    all_words = []\n",
    "    with open(path, \"r\", encoding=\"utf8\") as f:\n",
    "        for line in f:\n",
    "            words = line.split() + [\"<eos>\"]\n",
    "            all_words.extend(words)\n",
    "    return all_words\n",
    "\n",
    "tokenized_words = tokenize(PTB_TRAIN_PATH)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ESIZE = 100\n",
    "net, vocab, word_to_ix = train(tokenized_words, embedding_size=ESIZE, epochs=10)\n",
    "\n",
    "filename = 'ptb_word2vec_%d.pt' % 100\n",
    "torch.save(net.embeddings.weight, filename)\n",
    "print(\"Saving %s, size: %s\" % (filename, net.embeddings.weight.size()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FastText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "def visualize_embedding(embeddings):\n",
    "\n",
    "    fig, ax = plt.subplots(dpi=300)\n",
    "\n",
    "    plot_words = ['dog', 'cat', 'fish', 'cheese', 'bread', \n",
    "                  'colorado', 'travel', 'california', 'texas', 'apple',\n",
    "                  'nevada', 'mississippi', \"'s\", \"n't\", \"a\", \"the\", \"in\", \"to\",\n",
    "                  'lawsuit', 'legal', 'finance', 'financial', \n",
    "                  'money', 'currency', 'dollars', 'man', 'woman', 'boy', 'girl',\n",
    "                 'orange', 'institution', 'bank', 'donation', 'investment']\n",
    "    word_embeddings = []\n",
    "    found_words = []\n",
    "    for word in plot_words:\n",
    "        if word in embeddings:\n",
    "            word_embeddings.append(embeddings[word])\n",
    "            found_words.append(word)\n",
    "    word_embeddings = np.array(word_embeddings)\n",
    "    print(word_embeddings.shape)\n",
    "    pca = PCA(n_components=2)\n",
    "    embed_pca = pca.fit_transform(word_embeddings)\n",
    "\n",
    "    X = []\n",
    "    Y = []\n",
    "\n",
    "    for i, word in enumerate(found_words):\n",
    "        x, y = embed_pca[i, :]\n",
    "        X.append(x)\n",
    "        Y.append(y)\n",
    "        ax.text(x+0.03, y+0.03, word, fontsize=5)\n",
    "\n",
    "    ax.scatter(X, Y, s=1.0, alpha=0.5)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ptb_fasttext.bin\n"
     ]
    }
   ],
   "source": [
    "import fasttext\n",
    "\n",
    "model = fasttext.train_unsupervised(PTB_TRAIN_PATH, model='skipgram')\n",
    "filename = \"ptb_fasttext.bin\"\n",
    "print(\"Saved %s\" % filename)\n",
    "model.save_model(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-24f879f0ca41>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvisualize_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-1de80871127f>\u001b[0m in \u001b[0;36mvisualize_embedding\u001b[0;34m(embeddings)\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0mword_embeddings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mfound_words\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mword_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_embeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_embeddings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPCA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_embedding(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from ptb import lang_util\n",
    "import fasttext\n",
    "\n",
    "# corpus = lang_util.Corpus(\"/Users/jgordon/nta/datasets/PTB\")\n",
    "ft_model = fasttext.load_model(\"/Users/jgordon/nta/datasets/embeddings/ptb_fasttext.bin\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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'seizure', 'craven', 'tainted', 'applies', 'gubernatorial', 'quack', 'redemptions', 'diplomat', 'fairfield', 'bullets', 'fabrication', 'painfully', 'distorted', 'firstsouth', 'gutfreund', 'jetliner', 'arrogant', 'fights', 'colonial', 'bureaucrat', 'redevelopment', 'endure', 'eurobonds', 'dissent', 'quickview', 'dai-ichi', 'civilian', 'mid-1990s', 'kangyo', 'mansion', 'oat', 'secretaries', 'endless', 'christies', 'sink', 'condemn', 'digs', 'focuses', 'brilliant', 'money-losing', 'weird', 'beaten', 'backdrop', 'blessing', 'traub', 'management-led', 'abused', 'cilcorp', 'blind', 'egyptian', 'maurice', 'loath', 'mania', 'four-year-old', 'ernest', 'cascade', 'ex-dividend', 'bakker', 'child-care', 'bottled', 'insider-trading', 'lovely', 'grasp', 'marc', 'peripherals', 'company-owned', 'instrumental', 'allowance', 'misconduct', 'compelling', 'shannon', 'clobbered', 'lipton', 'avon', 'tramp', 'lavelle', 'lid', 'fund-raising', 'acquitted', 'assessed', 'prepayments', 'modify', 'multibillion-dollar', 'burmah', 'refiners', 'chair', 'edgar', 'justices', 'ohbayashi', 'faltered', 'enserch', 'posner', 'liberation', 'intervene', 'adversary', 'conception', 'two-tier', 'billion-dollar', 'n.v', 'boyer', 'inserted', 'potato', 'restricts', 'idaho', 'salvador', 'caci', 'uninsured', '13-week', 'deere', 'bare-faced', 'searched', 'wellcome', 'remedy', 'dallas-based', 'footing', 'conspired', 'dishonesty', 'aroused', 'atlas', 'recycling', 'blunt', 'brunt', \"o'connell\", 'kevin', 'balls', 'airbus', 'skyrocketed', 'outlawed', 'eroded', 'comsat', 'drum', 'harmony', 'hazard', 'matthews', 'trapped', 'relying', 'dogged', 'sporadic', 'natural-gas', 'dialysis', 'coates', 'wanting', 'property\\\\/casualty', 'michelin', 'cooling', 'abandoning', 'clean-air', 'chestman', 'poorer', 'presumed', 'mit', 'fray', 'centerpiece', 'broadway', 'tremor', 'alongside', 'depress', 'espn', 'recognizing', 'posed', 'reruns', 'bunny', '\\\\*\\\\*', 'switches', 'zip', 'viewpoint', 'sheep', 'drawings', 'simultaneous', 'coda', 'rocket', 'incorrect', 'revoke', 'hung', 'dlj', 'bronner', 'chicken', 'provinces', 'medication', 'exceptionally', 'tritium', 'tangible', 'hilton', 'irrelevant', 'mom', 'uncle', 'suites', 'primerica', 'overbuilt', 'foreclosed', 'mouth', 'high-interest', 'armco', 'barron', 'bomber', 'numbered', 'acknowledging', 'prescribed', 'yetnikoff', 'amicable', 'subpoena', 'vendors', 'nye', 'prohibition', 'sagged', 'brewery', 'colors', 'blanket', 'shelves', 'unfilled', 'sworn', 'haskins', 'h.h.', 'stevenson', 'elliott', 'reupke', 'cutler', 'iafp', 'pediatric', 'simpler', 'businessland', 'trecker', 'verge', 'f-14', 'killer', 'slim', 'butcher', 'satisfying', 'battled', 'short-lived', 'reuter', 'bougainville', 'cie.', 'booked', 'egypt', 'undoubtedly', 'reinvest', 'await', 'parsow', 'tightly', 'patience', 'bizarre', 'marched', 'benton', 'rooted', 'constructed', 'alabama', 'extract', 'soul', 'bozell', 'butter', 'alice', 'marble', 'mundane', 'frustrating', 'olympic', 'okla.', 's&ls', 'confessed', 'a.g.', 'classical', 'hangs', 'rep', 'fleets', 'dig', 'giorgio', 'collectors', 'wherever', 'micro', 'accessible', 'clifford', 'intentionally', 'unraveled', 'accustomed', 'rout', 'gently', 'flom', 'fearful', 'turnpike', 'convey', 'libya', 'agip', 'portrait', 'sloppy', 'trendy', 'depended', 'incredible', 'blaming', 'booklets', 'instruction', 'supplying', 'actress', 'liked', 'persian', 'roller', 'roller-coaster', 'diversity', 'recoup', 'stanza', 'enron', 'leaping', 'shanghai', 'first-time', 'adjacent', 'deliberations', 'discouraged', 'plumbing', 'vessel', 'diversion', 'format', '24-hour', 'cftc', 'anti-nuclear', 'lobbied', 'permitting', 'weakest', 'ala.', '45-year-old', 'delegates', 'disclosing', 'feat', 'committing', 'centered', 'havoc', 'grossly', 'pitching', 'wooden', 'conclusions', 'specialize', 'pete', 'racked', 'midmorning', 'cheered', 'educate', 'heroes', 'endanger', 'insight', '14-year-old', 'detergent', 'makeup', 'update', 'resilience', 'competitiveness', 'bail', 'pepsi', 'recycled', 'rode', 'koreans', 'accomplished', 'greens', 'neatly', 'neutral', 'prevailing', 'erased', 'butler', 'worrisome', 'scheduling', 'negotiator', 'rationale', 'twelve', 'parade', 'performer', 'excited', 'concert', 'knock', 'looming', 'excesses', 'lancaster', 'r.h.', 'contested', 'piano', 'retrieve', 'freed', 'reveals', 'clarify', 'shooting', 'blues', 'professors', 'loud', 'barnett', 'coated', 'chunks', 'upbeat', 'revise', 'naked', 'market-share', 'warehouses', 'define', 'tailored', 'dashed', 'reassuring', 'holt', 'grey', 'g.m.b', 'uneasy', 'carla', 'emigration', 'carr', 'ferry', 'refrigerators', 'outsider', 'restructurings', 'namibia', 'centuries', 'novelist', 'outperformed', 'teeth', 'brushed', 'balloting', 'western-style', 'commerzbank', 'bulls', 'tally', 'subdued', 'kan.', 'amortization', 'mighty', 'nonsense', 'gregory', 'incompetent', 'distributing', 'permanently', 'mice', 'prone', 'touted', 'termination', 'tacked', 'leipzig', 'arising', 'cocaine', 'magnified', 'decisive', 'lama', 'orderly', 'reimburse', 'emerson', 'aborted', 'dusty', 'legitimacy', 'peninsula', 'inventor', 'explicit', 'outperform', 'permissible', 'poised', 'restraints', 'microprocessors', 'fleischmann', 'construct', 'emergence', 'translation', 'southmark', 'bankruptcy-court', 'struggles', 'rattled', 'domestically', 'gartner', 'bowed', 'recital', 'telephones', 'yorker', 'hints', 'buffer', 'reinvested', 'byrd', 'rewrite', 'distributions', 'fleeting', 'interrupted', 'dial', 'backgrounds', 'danny', 'smiling', 'receiver', 'occasion', 'buffet', 'celebrating', 'cubans', 'blue-collar', '26-week', 'predictably', 'bets', 'runway', 'revolving', 'peat', 'downgrading', 'kohlberg', 'hastily', 'reeling', 'unstable', 'greedy', 'divergence', 'entrepreneurial', 'jeopardy', 'managua', 'sigh', 'sexual', 'shipment', 'wooing', 'disk-drive', 'russians', 'chlorofluorocarbons', 'doubtful', 'bond-equivalent', 'negligible', 'flamboyant', 'celebrity', 'invests', 'price-earnings', 'caller', 'pasta', 'chaotic', 'prints', 'shv', 'sharpest', 'profitably', 'geared', 'mimic', 'vivid', 'mo.', 'cancellation', 'bat', 'environmentally', 'andy', 'fad', 'whoever', 'cost-sharing', 'restricting', 'aeroflot', 'coping', 'backlash', 'playoffs', 'headaches', 'manpower', 'sits', 'drastic', 'plaintiff', 'steering', 'prediction', 'court-appointed', 'unicorp', 'cara', 'contrasts', 'wichita', 'infringed', 'warehouse', 'bargain-hunting', 'calculate', 'scary', 'till', 'dillon', 'equation', 'advertiser', 'baring', 'crandall', 'halts', 'behaved', 'hectic', 'syndicated', 'afloat', 'intensely', 'barrett', 'irish', 'plummet', 'swung', 'weirton', 'nagging', 'mmi', 'shouted', 'etc', 'inning', 'stir', 'trusted', 'showroom', 'johnston', 'hertz', 'besieged', 'subscription', 'influx', 'dignity', 'sporting', 'propane', 'nervously', 'jan', 'infringement', 'fossett', 'cubs', 'shah', 'black-and-white', 'tunnel', 'certified', 'non-interest', 'outset', 'farrell', 'rebounding', 'subway', 'on-site', 'geographic', 'elephant', 'recorder', 'dictaphone', 'arrow', 'bausch', 'nurses', 'bsn', 'rosenthal', 'lenses', 'callers', 'interactive', 'hut', 'novello', 'minimills', 'massage', 'massages', 'willful', 'ncr', 'justin', 'programmers', 'palmer', 'skipper', 'kia', 'memotec', 'mlx', 'ipo', 'nahb', 'punts', 'hydro-quebec', 'rake', 'guterman', 'gitano', 'regatta', 'fromstein', 'cluett', 'rubens', 'sim', 'centrust', 'biscuits', 'snack-food', 'ssangyong', 'calloway', 'swapo', 'photography', 'berlitz', 'wachter', 'flat-rolled', 'isi', 'daewoo', 'banknote', 'ferc', 'aer'], array([50770, 45020, 42068, ...,     1,     1,     1]))\n"
     ]
    }
   ],
   "source": [
    "print(ft_model.get_words('</s>'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.6982269 1.4514699\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "all_vals = []\n",
    "i = 0\n",
    "for word in ft_model.get_words():\n",
    "    all_vals.extend(ft_model[word])\n",
    "    if i > 100:\n",
    "        break\n",
    "\n",
    "print(min(all_vals), max(all_vals))\n",
    "\n",
    "plt.hist(all_vals)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = {}\n",
    "for word_id, word in enumerate(corpus.dictionary.idx2word):\n",
    "    embedded = ft_model[word]\n",
    "    embedding[word_id] = torch.tensor(embedded)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
